The University of Sydney - Engineering

Unit:	INFO5060: Data Analytics and Business Intelligence (6 CP)
Mode:	Block Mode
On Offer:	Yes
Level:	Postgraduate
Faculty/School:	School of Computer Science
Unit Coordinator/s:	A/Prof Poon, Simon Professor Davis, Joseph
Session options:	Summer Early
Versions for this Unit:	2020 - Int July 2020 - Int January 2019 - Summer Main 2018 - Summer Early 2017 - Summer Early 2016 - Summer Early 2015 - Summer Early 2014 - Summer Main Go
Site(s) for this Unit:	http://learn-on-line.ce.usyd.edu.au

Campus:	Camperdown/Darlington
Pre-Requisites:	None.
Brief Handbook Description:	The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.
Assumed Knowledge:	The unit is expected to be taken after introductory courses or related units such as COMP5206 Information Technologies and Systems

Timetable:	INFO5060 Timetable
Time Commitment:	# Activity Name Hours per Week Sessions per Week Weeks per Semester 1 Lecture 4.00 2 3 2 Tutorial 2.00 2 3 3 Laboratory 6.00 2 3 4 Presentation 3.00 1 1 5 Project Work - own time 6.00 3 6 Independent Study 3.00 3

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method	Attribute Developed
Analysing requirements, design and implement a Business Intelligence Dashboard solution	Design (Level 4)
Develop understanding the concepts and components of data analytics and business intelligence solution architecture	Engineering/IT Specialisation (Level 4)
Ability to independently research current state of knowledge on recent technical development of data analytics and impacts of business intelligence on organisational decision making	Information Seeking (Level 4)
Providing in-depth requirements gathering with a `customer` on a Business Intelligence Solution. Able to communicate extensive considerations made on theoretical and methodological issues regarding the solution proposed. Able to interpret and discuss issues and situations around the solution with due consideration of broad theoretical/practical contexts.	Communication (Level 4)
Develop professional decision-making in developing a business intelligence solution based on business requirements. Exercises sound critical judgement in undertaking the process and soft skills of building a business intelligence solution.	Professional Conduct (Level 4)
Perform major stages of a business intelligence Dashboard group project	Project and Team Skills (Level 4)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Design (Level 4)
Engineering/IT Specialisation (Level 4)
Information Seeking (Level 4)
Professional Conduct (Level 4)
Project and Team Skills (Level 4)
Communication (Level 4)

Assessment Methods:	# Name Group Weight Due Week Outcomes 1 Participation No 15.00 Multiple Weeks 1, 2, 3, 2 Team Assignment Yes 25.00 Week 3 1, 2, 3, 5, 3 Team Proposal and Presentation Yes 25.00 Week 3 2, 5, 4 Final Exam No 35.00 Week 3 1, 2,
Assessment Description:	The team assignment will require students to apply the knowledge and techniques covered in the course to develop a business intelligence solution based on a real world case study. Using an experiential learning approach, each team will use a Business Intelligence methodology to gather business requirements, design a solution and build a working prototype of a performance dashboard. To make the learning dynamic, the lecturer will role play the customer and provide information for the business requirements, and ongoing feedback as the teams’ design and build their solutions. There will be a strong focus on leveraging the industry expertise of the lecturer to coach the teams through the process and soft skills of building a business intelligence solution. The assessment deliverables will be based on both the BI Solution and a presentation. The final exam will be 1 hour long (with 10 minutes reading time) during the University Examination period. It will consist of 25 multiple choice questions and test the students knowledge of the course core concepts.
Grading:	Grade Type Description Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator. Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures:	IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism. In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so. Other policies See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.

Online Course Content:

http://learn-on-line.ce.usyd.edu.au

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week	Description
Week 1	Days 1 & 2 (First teaching block conducted in week 1 of summer semester) Day 1 (Module 1: Introduction to Data Analytics and Business Intelligence) • The history of Data Analytics and Business Intelligence • The business case for Business Intelligence • Organizational decision making • Examples of Business Intelligence in Action • Careers in Business Intelligence Day 2 (Module 2: Introduction to Dashboards) • Dashboard Concept • Dashboard Examples • SAP Dashboard Design • Dashboard Tutorial
Week 2	Days 3 & 4 (Second teaching block conducted in Week 2a of summer semester) Day 3 (Module 3: Dashboard Development Methodology) • Dashboards and Performance Management • Identifying and prioritizing Key Performance Indicators • Fundamentals of Dashboard design • Usability and dashboard design layout • Selecting the appropriate media for displaying data Day 4 (Module 4: BI Solution Architecture) • Components of a BI Solution • Data Sources • Data Integration • Data Quality • Data Warehouses • Reports and Dashboards Days 5 & 6 (Third Teaching Block conducted in Week 2b of summer semester) Day 5 (Module 5: Advanced Topics in BI) • BI Success and Change Management • BI Capability and Maturity Model Day 6 (Module 6: Current trends in BI) • Self Service BI • Real-Time BI • Mobility and BI
Week 3	Revision & Presentations
	Assessment Due: Team Assignment
	Assessment Due: Team Proposal and Presentation
	Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course	Year(s) Offered
Graduate Certificate in Data Science	2023, 2024, 2025
Graduate Diploma in Computing	2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Graduate Diploma in Data Science	2023, 2024, 2025
Graduate Diploma in Information Technology	2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Graduate Diploma in Complex Systems	2019, 2020, 2021, 2022, 2023, 2024, 2025
Master of Complex Systems	2021, 2022, 2023, 2024, 2025
Master of Data Science	2023, 2024, 2025
Master of Data Science (2022 and earlier)	2016, 2017, 2018, 2019, 2020, 2021, 2022
Master of Information Technology	2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Master of Information Technology Management	2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Master of IT / Master of IT Management	2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

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Course Goals

This unit contributes to the achievement of the following course goals:

Attribute	Practiced	Assessed
Design (Level 4)	Yes	27.5%
Engineering/IT Specialisation (Level 4)	Yes	43.75%
Information Seeking (Level 4)	Yes	10%
Professional Conduct (Level 4)	Yes	0%
Project and Team Skills (Level 4)	Yes	18.75%
Communication (Level 4)	Yes	0%
Maths/Science Methods and Tools (Level 3)	No	0%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.